Future of cardiac diffusion magnetic resonance imaging: a bibliometric analysis.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong, China.
- MSC Clinical & Technical Solutions, Philips Healthcare, Guangzhou, Guangdong, China.
- Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, Guangdong, China.
Abstract
Cardiac diffusion magnetic resonance imaging (cDMRI) has become a valuable noninvasive tool for assessing cardiac microstructure. To perform a systematic and comprehensive bibliometric analysis of cDMRI to explore current status, research hotspots and potential future directions. A systematic search of Web of Science Core Collection (WoSCC) and PubMed was conducted to identify articles on cDMRI published between 2005 and 2024. All retrieved records were independently screened by two reviewers to exclude the articles unrelated to the topic about cDMRI. Co-citation, collaboration networks, burst keywords, and reference analyses were performed. There were 359 articles searched in WoSCC and 88 articles in PubMed. Finally, 367 articles met the search criteria in both databases. The cardiac "diffusion tensor imaging" (cDTI) emerged as the most prominent keyword, with the highest occurrence count (191.0) and strongest citation bursts (6.8). The study by Nielles-Vallespin et al. showed the strongest citation burst (26.17), accumulating 163 total citations and an average of 20.38 citations per year, highlighting its substantial influence on the development of cDMRI research. The top-cited articles with the strongest citation bursts concentrated on the clinical application of cDTI, its sequence design, and cardiac electrophysiology based on cDTI. The trend topic analysis further revealed "deep learning" as an emerging research focus in cDMRI since 2022. To our knowledge, this is the first dedicated bibliometric analysis focusing on cDMRI. By systematically analyzing the current status, research hotspots, and future trends of cDMRI, it provided valuable insights to guide future investigations in this field.